Targeted therapy is a mechanism for using drugs designed to target specific biomarkers to treat a disease. Because the drugs are only effective in regard to a specific corresponding biomarker, targeted therapy requires an initial determination as to whether a patient will respond to a given drug treatment based upon whether or not the specific target biomarker is present within the diseased cells. Thus, the effectiveness of a drug depends on the level of expression of the biomarker in diseased cells and tissues taken as a sample from the patient.
A biomarker is a protein, peptide, sugar, RNA, deoxyribonucleic acid (DNA), or gene within cells or tissues. The expression of the biomarker correlates to a specific biologic event and/or response to treatment with a specific agent or drug.
The following examples illustrate a scenario in which estrogen receptor (a protein) expression, Her2 gene copy number, thymidylate synthase RNA levels, and p53 protein are all biomarkers. In the first example, an expression of Estrogen receptor in breast cancer cells correlates with better response to treatment in women with breast cancer treated with the drug, Tamoxifen, which targets the Estrogen receptor. As a second example, increased copy number (i.e., an increase in the number of copies) of the Her2 gene, or an increased Her2 protein, in breast cancer cells correlates with better survival (treatment response) in women with breast cancer treated with transtuzumab (e.g., Herceptin®), which targets the Her2 receptor. As a third example, expression of thymidylate synthase (TS) protein or RNA in colon cancer cells correlates with resistance to the drug 5-fluorouracil (5-FU). As a final example, an increased expression of p53 protein in a condition known as Barrett's esophagus correlates with increased risk for cancer of the esophagus. Although the above examples discusses treatment of cancer cells, biomarkers are useful in treating many different diseases.
In order to determine whether a targeted drug will be effective, a patient tissue sample is first evaluated to determine whether the target biomarker for a given drug is present within the tissue sample. Specifically, because not all cancer cells express the biomarker, whether a biomarker is present in the cancer cells needs to be first determined prior to determining whether the drug targeting the biomarker is effective. Specifically, some cells may completely lack the biomarker or express low quantities of the biomarker that are not sufficient to produce the desired effect when the drug is given.
For example, if drug “A” targets protein “a”, but biomarker analysis of the tissue shows only 40% of the cancer cells test positive for biomarker “a”, then a second drug must be used to address the remaining 60% of cells. In such a scenario, it is common for several drugs to be used in combination to address the patient-specific disease profile. Determination of the patient-specific disease profile is the goal of biomarker analysis.
Several methods exist to assess a single biomarker expression level in tissues and cell lines. The existing methods are successful to determine the likelihood of successful treatment using a single drug based upon the biomarker expression ratio specific to the single drug. However, when a single drug does not sufficiently treat all diseased cell classes, a drug profile corresponding to multiple biomarkers must be obtained in order to develop an adequate treatment plan employing a combination of drugs.
For example, to determine if a patient will respond to treatment with drugs “A”, “B”, “C” and/or “D”, a pathologist must first determine the patient-specific disease profile of the biomarker targets “a”, “b”, “c”, and “d”, respectively for each drug. Specifically, the pathologist determines the expression of, or the percentage of cells positive for each biomarker in the patient's tissue sample.
The above techniques, however, do not establish the co-expression relationship among biomarkers. To continue with the example, using current methods, the results could be reported as: 50% of the cancer cells are positive for biomarker “a”, 20% of cells are positive for “b”, 30% are positive for “c”, and 10% positive for “d”.
The results suggest that using all drugs in combination (A, B, C, and D) will kill all of the cancer cells. However, because of potential overlap between the cells having each biomarker, it is possible that not all cancer cells will be killed. For example, it is possible that all cells that are positive for “b” and “c” are also positive for “a” while cells positive for “d” do not co-express any other biomarker. In such a scenario, drugs “B” and “C” target diseased cells already addressed by drug “A”, and using all four drugs will kill only 60% of the cells (50% for “a,b,c” and 10% for “d”), possibly leaving 40% of the diseased cells free to proliferate and ultimately kill the patient.
Further, most cancer drugs have toxic side effects. Therefore, it is in the patient's best interest to optimize drug therapy in regard to i) effectiveness and ii) side effects. In our example, if drug “B” or “C” has adverse side effects, the physician could withhold both drugs and achieve the same treatment outcome while improving the patient's quality of life. However, the patient may still die because the therapy did not address a significant portion of diseased cells with the chosen drug combination.